Among algorithms for comparing the areas under two or more correlated receiver operating characteristic (ROC) curves, DeLong's algorithm is perhaps the most widely used one due to its simplicity of ...implementation in practice. Unfortunately, however, the time complexity of DeLong's algorithm is of quadratic order (the product of sample sizes), thus making it time-consuming and impractical when the sample sizes are large. Based on an equivalent relationship between the Heaviside function and mid-ranks of samples, we improve DeLong's algorithm by reducing the order of time complexity from quadratic down to linearithmic (the product of sample size and its logarithm). Monte Carlo simulations verify the computational efficiency of our algorithmic findings in this work.
The outbreak of an epidemic disease may pose significant treats to human beings and may further lead to a global crisis. In order to control the spread of an epidemic, the effective management of ...rapidly increased medical waste through establishing a temporary reverse logistics system is of vital importance. However, no research has been conducted with the focus on the design of an epidemic reverse logistics network for dealing with medical waste during epidemic outbreaks, which, if improperly treated, may accelerate disease spread and pose a significant risk for both medical staffs and patients. Therefore, this paper proposes a novel multi-objective multi-period mixed integer program for reverse logistics network design in epidemic outbreaks, which aims at determining the best locations of temporary facilities and the transportation strategies for effective management of the exponentially increased medical waste within a very short period. The application of the model is illustrated with a case study based on the outbreak of the coronavirus disease 2019 (COVID-19) in Wuhan, China. Even though the uncertainty of the future COVID-19 spread tendency is very high at the time of this research, several general policy recommendations can still be obtained based on computational experiments and quantitative analyses. Among other insights, the results suggest installing temporary incinerators may be an effective solution for managing the tremendous increase of medical waste during the COVID-19 outbreak in Wuhan, but the location selection of these temporary incinerators is of significant importance. Due to the limitation on available data and knowledge at present stage, more real-world information are needed to assess the effectiveness of the current solution.
Multistage stochastic integer programming (MSIP) combines the difficulty of uncertainty, dynamics, and non-convexity, and constitutes a class of extremely challenging problems. A common formulation ...for these problems is a dynamic programming formulation involving nested cost-to-go functions. In the linear setting, the cost-to-go functions are convex polyhedral, and decomposition algorithms, such as nested Benders’ decomposition and its stochastic variant, stochastic dual dynamic programming (SDDP), which proceed by iteratively approximating these functions by cuts or linear inequalities, have been established as effective approaches. However, it is difficult to directly adapt these algorithms to MSIP due to the nonconvexity of integer programming value functions. In this paper we propose an extension to SDDP—called stochastic dual dynamic integer programming (SDDiP)—for solving MSIP problems with binary state variables. The crucial component of the algorithm is a new reformulation of the subproblems in each stage and a new class of cuts, termed Lagrangian cuts, derived from a Lagrangian relaxation of a specific reformulation of the subproblems in each stage, where local copies of state variables are introduced. We show that the Lagrangian cuts satisfy a tightness condition and provide a rigorous proof of the finite convergence of SDDiP with probability one. We show that, under fairly reasonable assumptions, an MSIP problem with general state variables can be approximated by one with binary state variables to desired precision with only a modest increase in problem size. Thus our proposed SDDiP approach is applicable to very general classes of MSIP problems. Extensive computational experiments on three classes of real-world problems, namely electric generation expansion, financial portfolio management, and network revenue management, show that the proposed methodology is very effective in solving large-scale multistage stochastic integer optimization problems.
Abstract
Severe events of wintertime particulate air pollution in Beijing (winter haze) are associated with high relative humidity (RH) and fast production of particulate sulfate from the oxidation ...of sulfur dioxide (SO
2
) emitted by coal combustion. There has been considerable debate regarding the mechanism for SO
2
oxidation. Here we show evidence from field observations of a haze event that rapid oxidation of SO
2
by nitrogen dioxide (NO
2
) and nitrous acid (HONO) takes place, the latter producing nitrous oxide (N
2
O). Sulfate shifts to larger particle sizes during the event, indicative of fog/cloud processing. Fog and cloud readily form under winter haze conditions, leading to high liquid water contents with high pH (>5.5) from elevated ammonia. Such conditions enable fast aqueous-phase oxidation of SO
2
by NO
2
, producing HONO which can in turn oxidize SO
2
to yield N
2
O.This mechanism could provide an explanation for sulfate formation under some winter haze conditions.
Unit commitment (UC) is a key operational problem in power systems for the optimal schedule of daily generation commitment. Incorporating uncertainty in this already difficult mixed-integer ...optimization problem introduces significant computational challenges. Most existing stochastic UC models consider either a two-stage decision structure, where the commitment schedule for the entire planning horizon is decided before the uncertainty is realized, or a multistage stochastic programming model with relatively small scenario trees to ensure tractability. We propose a new type of decomposition algorithm, based on the recently proposed framework of stochastic dual dynamic integer programming (SDDiP), to solve the multistage stochastic unit commitment (MSUC) problem. We propose a variety of computational enhancements to SDDiP, and conduct systematic and extensive computational experiments to demonstrate that the proposed method is able to handle elaborate stochastic processes and can solve MSUCs with a huge number of scenarios that are impossible to handle by existing methods.
Recent years have witnessed the flourishing of deep learning-based methods in hyperspectral anomaly detection (HAD). However, the lack of available supervision information persists throughout. In ...addition, existing unsupervised learning/semisupervised learning methods to detect anomalies utilizing reconstruction errors not only generate backgrounds but also reconstruct anomalies to some extent, complicating the identification of anomalies in the original hyperspectral image (HSI). In order to train a network able to reconstruct only background pixels (instead of anomalous pixels), in this article, we propose a new blind-spot self-supervised learning network (called BS3LNet) that generates training patch pairs with blind spots from a single HSI and trains the network in self-supervised fashion. The BS3LNet tends to generate high reconstruction errors for anomalous pixels and low reconstruction errors for background pixels due to the fact that it adopts a blind-spot architecture, i.e., the receptive field of each pixel excludes the pixel itself and the network reconstructs each pixel using its neighbors. The above characterization suits the HAD task well, considering the fact that spectral signatures of anomalous targets are significantly different from those of neighboring pixels. Our network can be considered a superb background generator, which effectively enhances the semantic feature representation of the background distribution and weakens the feature expression for anomalies. Meanwhile, the differences between the original HSI and the background reconstructed by our network are used to measure the degree of the anomaly of each pixel so that anomalous pixels can be effectively separated from the background. Extensive experiments on two synthetic and three real datasets reveal that our BS3LNet is competitive with regard to other state-of-the-art approaches.
Conducting hydrogels have attracted much attention for the emerging field of hydrogel bioelectronics, especially poly(3,4‐ethylenedioxythiophene): poly(styrene sulfonate) (PEDOT:PSS) based hydrogels, ...because of their great biocompatibility and stability. However, the electrical conductivities of hydrogels are often lower than 1 S cm−1 which are not suitable for digital circuits or applications in bioelectronics. Introducing conductive inorganic fillers into the hydrogels can improve their electrical conductivities. However, it may lead to compromises in compliance, biocompatibility, deformability, biodegradability, etc. Herein, a series of highly conductive ionic liquid (IL) doped PEDOT:PSS hydrogels without any conductive fillers is reported. These hydrogels exhibit high conductivities up to ≈305 S cm−1, which is ≈8 times higher than the record of polymeric hydrogels without conductive fillers in literature. The high electrical conductivity results in enhanced areal thermoelectric output power for hydrogel‐based thermoelectric devices, and high specific electromagnetic interference (EMI) shielding efficiency which is about an order in magnitude higher than that of state‐of‐the‐art conductive hydrogels in literature. Furthermore, these stretchable (strain >30%) hydrogels exhibit fast self‐healing, and shape/size‐tunable properties, which are desirable for hydrogel bioelectronics and wearable organic devices. The results indicate that these highly conductive hydrogels are promising in applications such as sensing, thermoelectrics, EMI shielding, etc.
PEDOT:PSS hydrogels exhibit high conductivities up to ≈305 S cm−1, which is ≈8 times higher than the record of polymeric hydrogels without conductive fillers. The high electrical conductivity results in enhanced areal thermoelectric output power for hydrogel‐based thermoelectric devices, and high specific electromagnetic interference shielding efficiency (an order in magnitude higher than that of reported of state‐of‐the art conductive hydrogels).
A vast area in China is currently going through severe haze episodes with
drastically elevated concentrations of PM2.5 in winter. Nitrate and
sulfate are the main constituents of PM2.5, but their ...formations via
NO2 and SO2 oxidation are still not comprehensively understood,
especially under different pollution or atmospheric relative humidity (RH)
conditions. To elucidate formation pathways of nitrate and sulfate in
different polluted cases, hourly samples of PM2.5 were collected
continuously in Beijing during the wintertime of 2016. Three serious
pollution cases were identified reasonably during the sampling period, and
the secondary formations of nitrate and sulfate were found to make a
dominant contribution to atmospheric PM2.5 under the relatively high RH
condition. The significant correlation between NOR, NOR = NO3-/(NO3-+NO2), and NO22 × O3 during the nighttime under the RH≥60 % condition indicated
that the heterogeneous hydrolysis of N2O5 involving aerosol
liquid water was responsible for the nocturnal formation of nitrate at the
extremely high RH levels. The more often coincident trend of NOR and HONO × DR (direct radiation) × NO2 compared to its occurrence with Dust × NO2 during the daytime under the 30 % < RH < 60 % condition provided convincing evidence that the gas-phase
reaction of NO2 with OH played a pivotal role in the diurnal formation
of nitrate at moderate RH levels. The extremely high mean values of SOR, SOR = SO42-/(SO42-+SO2), during the whole day
under the RH≥60 % condition could be ascribed to the evident
contribution of SO2 aqueous-phase oxidation to the formation of sulfate
during the severe pollution episodes. Based on the parameters measured in
this study and the known sulfate production rate calculation method, the
oxidation pathway of H2O2 rather than NO2 was found to
contribute greatly to the aqueous-phase formation of sulfate.
Hyperspectral target detection can be described as locating targets of interest within a hyperspectral image based on prior information of targets. The complexity of actual scenes limits the ...performance of traditional statistical methods that rely on model assumptions, and traditional machine learning methods rely on mapping functions with limited complexity. To address these problems, we propose a Siamese transformer network for hyperspectral image target detection (STTD). The contribution of this article is threefold. First, we propose a novel method of constructing training samples using only the image itself and the limited prior information, which is suitable for target detection based on the Siamese network framework. Second, the Siamese network framework is utilized to solve the problem of similarity metric learning, i.e., make homogeneous features as close as possible and heterogeneous features as far as possible. Third, the most state-of-the-art network, transformer, is applied as the backbone of our proposed Siamese network to extract global features from spectra with long-range dependencies to achieve target detection. Furthermore, we make adaptive improvements to transformer for hyperspectral images. The proposed method shows its unique advantages in suppressing the background to a low level and highlighting the target with high probability. Experiments on five different datasets demonstrate the superiority of the proposed STTD as compared to the state-of-the-art.
Predicting the effects of rising temperature entails measuring both habitat thermal characteristics and the physiological variation of the species as it relates to this microhabitat variation; these ...two types of measurements can generate what is termed a ‘physiological landscape’ for the species. Mapping the micro‐scale physiological landscape across space and time, rather than relying on large‐scale averages of temperature and means of thermal limits in a species, can allow more accurate estimates of an organism's sensitivity to temperature change and support development of more refined models of the impacts of anthropogenic climate change that have higher predictive power.
We thus continually monitored the body (operative) temperature of the intertidal mussel Mytilisepta virgata in both sun‐exposed and shaded microhabitats and determined the seasonal variations of cardiac performance of field‐acclimatized and laboratory‐acclimated mussels from different microhabitats for calculating the thermal sensitivity, as indicated by the difference between the maximum ambient temperature and an individual's upper thermal limit (thermal safety margin, TSM), in each microhabitat and each month.
The mussels experienced divergent thermal stress, in average temperature, acute and chronic thermal stress and thermal predictivity among different microhabitats, and presented high spatial–temporal variations of cardiac function as results of seasonal acclimatization and inter‐individual variations. Results of TSMs indicated that the thermal sensitivities of the mussels to high temperature were season‐ and microhabitat specific, and the mussels in the shaded microhabitats were predicted to survive the hottest summer temperatures; however, some individuals in the sun‐exposed microhabitats experienced temperatures above their sublethal temperature.
With the large, high‐resolution dataset of thermal environmental characteristics and the cardiac performances with high variations, we were able to integrate the effects of synchronized changes in microenvironmental temperatures and cardiac thermal responses and thereby characterize the physiological landscape of thermal sensitivity. The complex physiological landscape that exists in the intertidal zone must be taken into account when predicting the effects of changes in environmental temperature, such as those occurring with global climate change.
A free Plain Language Summary can be found within the Supporting Information of this article.
摘要
评估和预测气候变化的生态效应需要测定栖息地温度特征及生物生理耐受水平, 计算生物对环境温度变化的敏感性, 进而绘制 ‘生理景观’ 格局。通过分析小时空尺度生理景观的变化, 而不是单纯依赖于大尺度环境温度平均值和生物温度耐受能力的平均值, 可更为准确地预测生物对温度变化的敏感性及气候变化的生态效应。
本研究连续测定了潮间带条纹隔贻贝阴阳面不同微生境温度变化特征, 并分析了不同微环境生物贝类的心脏性能, 计算不同季节、不同微环境生物的温度安全区间 (Thermal safety margin, TSM), 以阐明小尺度生物温度敏感性的时空格局。
条纹隔贻贝在不同微生境面临着不同强度的温度胁迫, 呈现出高度的时空异质性。条纹隔贻贝心脏性能也表现出了高度的可塑性, 这主要与不同驯化温度和个体间差异有关。TSM结果表明, 贻贝对高温的敏感性是具有季节特异性和微生境特异性。在阴面微生境, 所有的条纹隔贻贝都可以在高温季节存活, 然后在阳面微生境, 部分个体可能会由于环境温度高于其亚致死温度而受到生理损害。
基于高清晰度的环境温度数据和生物心脏性能数据库, 可整合分析微生境温度与心脏性能曲线可塑性的生态效应, 评估生物对环境温度的敏感性。因此在评估和预测气候变化生态效应时, 需要充分考虑生理景观格局的复杂性及其时空变化格局。
A free Plain Language Summary can be found within the Supporting Information of this article.
PDF
